Predicting histological grade in pediatric glioma using multiparametric radiomics and conventional MRI features

Prediction of glioma is crucial to provide a precise treatment plan to optimize the prognosis of children with glioma. However, studies on the grading of pediatric gliomas using radiomics are limited. Meanwhile, existing methods are mainly based on only radiomics features, ignoring intuitive information about tumor morphology on traditional imaging features. This study aims to utilize multiparametric magnetic resonance imaging (MRI) to identify high-grade and low-grade gliomas in children and establish a classification model based on radiomics features and clinical features. A total of 85 children with gliomas underwent tumor resection, and part of the tumor tissue was examined pathologically. Patients were categorized into high-grade and low-grade groups according to World Health Organization guidelines. Preoperative multiparametric MRI data, including contrast-enhanced T1-weighted imaging, T2-weighted imaging, T2-weighted fluid-attenuated inversion recovery, diffusion-weighted images, and apparent diffusion coefficient sequences, were obtained and labeled by two radiologists. The images were preprocessed, and radiomics features were extracted for each MRI sequence. Feature selection methods were used to select radiomics features, and statistically significant clinical features were identified using t-tests. The selected radiomics features and conventional MRI features were used to train the AutoGluon models. The improved model, based on radiomics features and conventional MRI features, achieved a balanced classification accuracy of 66.59%. The cross-validated areas under the receiver operating characteristic curve for the classifier of AutoGluon frame were 0.8071 on the test dataset. The results indicate that the performance of AutoGluon models can be improved by incorporating conventional MRI features, highlighting the importance of the experience of radiologists in accurately grading pediatric gliomas. This method can help predict the grade of pediatric glioma before pathological examination and assist in determining the appropriate treatment plan, including radiotherapy, chemotherapy, drugs, and gene surgery.

Gliomas are the most common brain tumor among children, with a high incidence rate according to the Central Brain Tumor Registry of the United States 1 .These tumors are classified into two grades: pediatric low-grade tumors (pLGGs) and pediatric high-grade gliomas (pHGGs) 2 .While most children with low-grade gliomas have good overall survival, high-grade gliomas have a poor prognosis and are essentially incurable.Therefore, accurate prediction of glioma is crucial to provide a precise treatment plan to optimize the prognosis of children with glioma 3,4 .Currently, histopathological assessment after surgery or biopsy is the gold standard for glioma grading 5 , but it is challenging to accurately identify the pathological grade from preoperative clinical data.
Gliomas can be visualized on magnetic resonance imaging (MRI).Traditional MRI features capture clinically relevant characteristics observable through conventional radiology assessments by radiologist.For instance, enhancement pattern characterizes the enhancement intensity and pattern post-contrast administration on contrast-enhanced T1-weighted imaging (T1ce), which provides crucial insights into tumor vascularity and enhancement 6,7 .Peritumoral edema can typically be observed on T2-weighted imaging (T2WI) sequences.In www.nature.com/scientificreports/T2WI, areas of edema appear as regions of increased signal intensity surrounding the tumor, providing valuable information about the extent and characteristics of the edema surrounding the tumor 8,9 .These imaging features, such as tumor size, location, morphology, enhancement pattern, and peritumoral edema, provide crucial contextual information for pediatric glioma grading.However, traditional MRI features are typically derived from subjective observations and descriptions made by radiologists.While these features offer crucial clinical information, they lack quantitative numerical representation, making them challenging to directly compute and analyze statistically during the analysis process.
Radiomics features extract quantitative characteristics from large amounts of imaging data, including texture features, shape features, and intensity histograms 10,11 , which can provide deeper insights into the histological and biological properties of the tumor 12 .These features can capture subtle variations and irregular patterns in the tumor, enhancing the overall understanding of its characteristics.Recently, radiomics and machine learning techniques have shown great promise in the prediction of glioma grading 13,14 .However, research on the grading of pediatric gliomas using radiomics is limited.Meanwhile, existing methods are mainly based on only radiomics features, ignoring intuitive information about tumor morphology on traditional imaging features.
This study aims is to develop a machine learning model that can incorporate radiomics features and conventional MRI features for improving the prediction of histological grade.The machine learning model will be trained using multiparametric MRI from patients with pediatric glioma.By combining traditional imaging features and radiomics features, we can complement each other's limitations and improve the accuracy and reliability of grading.Traditional imaging features provide foundational information, while radiomics features offer quantitative details, allowing for a more comprehensive depiction of the tumor's characteristics and enabling more precise grading results.

Characteristics of the study cohort
This study analyzed a cohort of 85 patients with gliomas, comprising of 49 males and 36 females aged 0-16 years old.Among them, 28 patients had high-grade gliomas (33%), while 57 patients had low-grade gliomas (67%).No significant differences were found in age and gender between the two groups.The clinical information (gender and age) and tumor characteristics (pathological grades and conventional MRI features) are presented in Table 1.

Selected radiomics features
Radiomics features extracted from each MRI sequence were helpful in grading pediatric gliomas.The importance ranking of radiomics features is shown in Fig. 1, with the TOP 10 radiomics features listed below: These TOP 10 radiomics features included three ADC (apparent diffusion coefficient ) wavelet features, two CET1WI (contrast-enhanced T1-weighted imaging) wavelet features, two T2 FLAIR (T2-weighted fluidattenuated inversion recovery) wavelet features, two from diffusion-weighted images (DWI) wavelet features, and one T2 original feature.

Selected conventional MRI features
The conventional MRI features were selected using t-tests, and significant differences were observed between pLGGs and pHGGs groups for diffusion restriction and ring enhancement (diffusion restriction: p < 0.001; ring enhancement: p = 0.0307).The detailed statistical results of tumor characteristics are presented in Table 1.Diffusion restriction was mainly observed as hyperintensity at the tumor on DWI images, as demonstrated in Fig. 2c.Whereas ring enhancement was mainly observed as one or more ring-shaped enhancement foci of the tumor on CET1WI image as illustrated in Fig. 2d.
The features types used to train the classifier model were shown in Table 2.The feature set

Performance of the classification model
To evaluate the effectiveness of radiomics features in grading pediatric gliomas, we employed AutoGluon-based model and SVM-based model (such as C-SVC and Nu-SVC).Table 3 shows the classification performance of C-SVC, Nu-SVC and AutoGluon using radiomics features.Compared to the traditional SVM-based classifier model, the AutoGluon model achieves better performance.The AUC of glioma grading using the AutoGluon model is 0.8071, while the classification accuracy of the two SVM-based classifiers is 0.7742 (C-SVC) and 0.7484  pHGGs with histological grade of III-IV typically exhibit characteristics of restricted diffusion and ringenhancement on imaging.For instance, due to the large size of tumor cell nuclei and dense cell arrangement, the intercellular and extracellular spaces are narrow, resulting in restricted diffusion of water molecules.Moreover, the high metabolic rate of tumor cells and the tight accumulation of cells around the necrotic area promote the aggregation of neovascularization, typically appearing as thick-walled ring enhancement in the enhanced images of high-grade tumors.These two conventional MRI features can help distinguish high-grade tumors.3b and c.These results demonstrate that the classifiers with the highest AUC values are NeuralNetFastAl (AUC = 0.8424), followed by ExtraTreesGini (AUC = 0.8333) and ExtraTreesEntr (AUC = 0.8189).Figure 4 displays the model importance and performance of base classifiers in AutoGluon framework.These results demonstrate that the NeuralNetFastAI classifier model achieved the best performance in pediatric glioma grading.Additionally, conventional MRI features were combined with radiomics features to improve the classification performance.Table 5 illustrates the classification performance of different feature types using ExtraTressGini model.Figure 3d shows the ROC curves of NeuralNetFastAI model using different feature types.For feature type F 3 (radiomics features + diffusion restriction + ring enhancement), NeuralNetFastAl achieves the highest AUC of 0.8424, indicating the best performance.For feature type F 2 (Radiomics features + ring enhancement), NeuralNetFastAl achieves an AUC of 0.8394, showing slightly lower performance compared to type 3.For feature type F 1 (Radiomics features + diffusion restriction), NeuralNetFastAl achieves an AUC of 0.8409, performing similarly to type F 2 .For feature type F 0 (only radiomics features), NeuralNetFastAl achieves the lowest AUC of 0.8182.The experimental results indicate the varying impact of different feature types on the performance of the NeuralNetFastAl classifier.These results show that the performance of pediatric glioma grading using AutoGluon is improved by combining radiomics features and conventional MRI features.

Discussion
Pediatric gliomas not only differ significantly from adult gliomas in terms of incidence, site of occurrence, and prognosis but also exhibit obvious differences in terms of pathogenesis and molecular characteristics.Therefore, the grading model for adult gliomas cannot be simply applied to children.Developing a preoperative grading classification model for pediatric gliomas is of great value for improving the prognosis of affected children.
In this study, we developed and validated a classification model that enables the integration of radiomics features and conventional MRI features to improve the predication of histological grades in pediatric glioma.First, our AutoGluon grading model for pediatric glioma grading was superior to SVM-based classification models.Second, the expertise of experienced radiologist plays an important role in the grading of pediatric gliomas.The experiment results confirm this point that incorporating conventional MRI features with radiomics features can help improve the classification accuracy of machine learning model.Finally, our results show promise of our AutoGluon grading model and potential utilities for pediatric glioma histological grades predicting.
Radiomics features offer crucial information for glioma grading.These features comprise traditional image features such as first-order gradient features, shape features, texture features, as well as various features after geometric filtering and transformation.After feature selection, thirty radiomics features were reserved and ranked based on their frequency of occurrence using in classification experiment, as shown in Fig. 1.The TOP 10 radiomics features included all five modality (CET1WI, T2WI, T2 FLAIR, DWI, and ADC sequences) images, indicating that each modality contributes to pediatric glioma grading.And nine of the TOP 10 radiomics features were wavelet features, suggesting that wavelet texture features can provide multiple frequencies and scales information about tumor heterogeneity using wavelet decomposition.www.nature.com/scientificreports/While the development of radiomics technology has indeed influenced the grading methods for pediatric gliomas, traditional MRI features still hold significance in the grading process.Firstly, traditional MRI features provide essential information regarding the extent of tumor infiltration, compression of adjacent structures, and presence of necrosis, all of which contribute to the overall assessment of tumor aggressiveness and prognosis 8,9 .Additionally, traditional MRI features are readily identifiable and interpretable by radiologists, facilitating quick and accurate diagnosis in clinical practice.Therefore, traditional MRI features remain relevant and complementary in the assessment of pediatric gliomas, providing valuable insights into tumor characteristics and behavior.
In this study, significant differences between pHGGs and pLGGs can be observed on MRI images for restricted diffusion and ring enhancement.Restricted diffusion can provide information of the diffusion of microscopic water molecules in normal and diseased tissues 15,16 .In malignant tumors, due to the large nuclei of tumor cells, increased nuclear-to-cytoplasmic ratio, and tight cell arrangement, the intracellular and extracellular spaces are narrowed, and the diffusion of water molecules is restricted.Whereas in benign tumors, due to the characteristics of low cell density and loose cytoplasm, the diffusion of water molecules is generally not restricted, and tumor lesions show low signal on DWI 13 .In this study, the classification performance has been improved by using diffusion restriction combined with radiomics characteristics in pediatric gliomas grading, indicating that limited diffusion can be used as an important clinical indicator for differentiating the grading of childhood gliomas.
The high metabolic rate and tight cell packing of tumor cells promote the accumulation of new blood vessels around necrotic areas, which are usually thick-walled ring enhancements on enhanced images 14 .This enhancement pattern can be seen in high-grade astrocytoma and anaplastic ependymoma.Wu et al. combined ring enhancement with radiomics features to predict H3K27M mutation in childhood high-grade gliomas, and concluded that ring enhancement can be used as a clinical predictor of H3K27M mutation 17 .Meanwhile, Chiang   18,19 .In conclusion, ring enhancement can also be a predictor of molecular mutation and prognosis of glioma.However, studies using ring enhancement as a predictor of the pediatric glioma grading are rare.This study shows that the combination of clinical factors and radiomic features can better predict the pediatric gliomas grades than using radiomic features alone.There are several limitations in this study.First, more data from several centers needs continually collecting.Second, although the World Health Organization includes molecular type information for glioma grading in 2021.This study data was collecting from 2015 to 2022, and only part of participants had molecular type information, so the classification of pHGGs and pLGGs was based on histological grade.In fact, molecular type and histological grade are both essential for analysis of glioma in clinical situation.Third, although radiomics are useful for pediatric gliomas grading, the tumors exhibit high heterogeneity as pilocytic astrocytoma.Further research is needed to explore the relationship between radiomics features and tumor heterogeneity by using advanced MRI sequences, including insights into white matter fiber orientation and integrity (DTI), tumor hemodynamics (DSCE), and tumor metabolism (MRS).By integrating these data, it becomes possible to get information about the orientation and integrity of white matter fibers, the hemodynamic characteristics of tumors, and the information about tumor metabolism 20 .Fourth, the tumor region defined in this study includes cystic components, necrosis, and hemorrhage, which corresponds to the "TC" region (the solid part of the tumor) defined in the BRATS public dataset (shown in Fig. 5).In following research, we will investigate the association between the tumor edema region and the pediatric gliomas grades.
In conclusion, we developed an AutoGluon model using multiparametric radiomics features and conventional MRI features to predict the histological grade of pediatric gliomas in this study.The model demonstrated outstanding classification performance in distinguishing between pHGGs and pLGGs.The results show that incorporating conventional MRI features can help improve the classification performance, indicating the crucial role of clinical factors in accurately assessing glioma grade.This method can assist clinicians in predicting the histological grade of gliomas prior to surgery, making treatment decisions of radiotherapy, chemotherapy, targeted drugs, and genetic surgery.

Figure 1 .
Figure 1.The importance ranking of radiomics features and the TOP 10 radiomics features.

Figure 2 .
Figure 2. The visualization of original T2WI images, VOI, diffusion restriction, and ring enhancement for different patients with glioma.The histological grades from left to right is grade III, III, IV, and IV, respectively.pHGGswith histological grade of III-IV typically exhibit characteristics of restricted diffusion and ringenhancement on imaging.For instance, due to the large size of tumor cell nuclei and dense cell arrangement, the intercellular and extracellular spaces are narrow, resulting in restricted diffusion of water molecules.Moreover, the high metabolic rate of tumor cells and the tight accumulation of cells around the necrotic area promote the aggregation of neovascularization, typically appearing as thick-walled ring enhancement in the enhanced images of high-grade tumors.These two conventional MRI features can help distinguish high-grade tumors.

Figure 3 .
Figure 3.The receiver operating characteristic (ROC) curves.(a) The ROC curves of C-SVC, Nu-SVC and AutoGluon framework, (b,c) The ROC curves of the classifier models under the AutoGluon framework, and (d) The ROC curves of the NeuralNetFastAI model using different feature types.

Figure 4 .
Figure 4. Importance of multimodal variables and performance of base machine learning models predicting histological grade of pediatric Glioma.The model importance is generated by the formula: importance = (xi − x)/σ, where xi is the ACC of each model dedicated, and x and σ are the mean and standard deviation of all ACCs, respectively.

Participants
We collected clinical information and MRI data from 91 patients diagnosed with grade I-IV gliomas according to the World Health Organization (WHO) Classification of Tumors of the Central Nervous System 2 , from 2015 to 2022.Patients with WHO grade I and grade II gliomas were classified as pediatric low-grade glioma (pLGGs), while patients with WHO grade III and grade IV gliomas were classified as pediatric high-grade glioma (pHGGs).This study was approved by the Ethics Committee of the Children's Hospital of Soochow University.And written informed consent was obtained from all participants.The study was conducted according to the latest version of the Declaration of Helsinki.

Figure 5 .
Figure 5. Visualization of the differences in tumor region definition between this study and BRATS public dataset.The two rows represent two subjects.In the VOI (Volume of Interest) images, the red region represents the tumor region defined in this study, while the green region represents the peritumoral edema.The labels in BRATS include the peritumoral edema, whereas in this study, the tumor region mainly refers to the intratumoral necrotic, cystic degeneration, and hemorrhagic.

Figure 7 .
Figure 7.The entire flowchart of classifier modeling using radiomics features and conventional MRI features for prediction pHGGs and pLGGs.

Table 1 .
The clinical information and tumor characteristic of the whole cohort.

Table 2 .
Details of the radiomics features and conventional MRI features used in training of the AutoGluon classifier.
0.8424, 0.8333, 0.8196, 0.8189, and 0.8189, respectively.Table4displays the classification performance of the five classifier models under the AutoGluon framework using radiomics features for predicting histological grade.Figure3ashows the receiver operating characteristic (ROC) curves of the five classifier models that performs best under the AutoGluon framework.Different sets of features are used to train machine learning models.AutoGluon-R only uses Radiomic features, AutoGluon-C only uses Conventional MRI features, while AutoGluon-RC uses both Radiomic features and Conventional MRI features.The experimental results show that among the classifiers used, AutoGluon-RC performed the best with an AUC value of 0.8071, slightly higher than AutoGluon-R's 0.8057.The AUC values for CSVC and NuSVC are 0.7742 and 0.7484, respectively, slightly lower than those of the AutoGluon series.AutoGluon-C achieved the lowest AUC value of 0.6816.These results indicate that AutoGluon-RC has demonstrated the most superior performance in this classification task.The experimental results using the AutoGluon classification framework with different base classifiers for pediatric glioma grading are shown in Fig.

Table 3 .
Classification performance of classifier of C-SVC, Nu-SVC and AutoGluon frame using different features.Feature type: -R = Radiomic features, -C = Conventional MRI features, -RC = Radiomic features + Conventional MRI features.

Table 6 .
The number of LGGs and HGGs in each of the 10 train-test splits.